Title: Generative Super-Resolution PET Imaging with Fourier Diffusion Models Show Chinese title

Title: 基于傅里叶扩散模型的生成式超分辨率正电子发射断层扫描成像

Abstract: Neurological Positron Emission Tomography (PET) is a critical imaging modality for diagnosing and studying neurodegenerative diseases like Alzheimer's disease. However, the inherent low spatial resolution of PET images poses significant challenges in clinical settings. This work introduces a novel Generative Super-Resolution (GSR) approach using Fourier Diffusion Models (FDMs) to enhance the spatial resolution of PET images. Unlike traditional methods, FDMs leverage the time-dependent Modulation Transfer Function (MTF) and Noise Power Spectrum (NPS) to generate high-resolution, low-noise images from low-resolution inputs. Our method was evaluated using simulated data derived from High-Resolution Research Tomograph (HRRT) PET images with 2 mm resolution. The results demonstrate that FDMs significantly outperform existing techniques, including conditional diffusion models and image-to-image Schr\"odinger bridge, across several metrics, including structural similarity and noise suppression. Our simulation results highlight the potential of FDMs to generate high-quality 2mm resolution reconstructions given 4mm resolution input PET data. Show Chinese abstract

Abstract: 神经学正电子发射断层扫描（PET）是诊断和研究神经退行性疾病如阿尔茨海默病的关键成像方式。 然而，PET图像固有的低空间分辨率在临床环境中带来了重大挑战。 本工作引入了一种新的生成超分辨率（GSR）方法，使用傅里叶扩散模型（FDMs）来提高PET图像的空间分辨率。 与传统方法不同，FDMs利用时间依赖的调制传递函数（MTF）和噪声功率谱（NPS），从低分辨率输入生成高分辨率、低噪声的图像。 我们的方法使用从高分辨率研究断层扫描仪（HRRT）PET图像（2毫米分辨率）中衍生的模拟数据进行评估。 结果表明，FDMs在多个指标上显著优于现有技术，包括条件扩散模型和图像到图像的薛定谔桥，包括结构相似性和噪声抑制。 我们的仿真结果突显了FDMs在给定4毫米分辨率PET数据的情况下生成高质量2毫米分辨率重建的潜力。